Recognize the Need for Document Databases

This course delves into the characteristics of document databases, their use cases, and the fundamentals of data modeling for such systems. You will cover the trade-offs involved in terms of storage and retrieval, and modeling relationships.
Course info
Level
Beginner
Updated
Sep 18, 2020
Duration
1h 40m
Table of contents
Description
Course info
Level
Beginner
Updated
Sep 18, 2020
Duration
1h 40m
Description

Document databases have been gaining in popularity for a number of years now and they are widely available - as tools which can be downloaded and installed on your own servers, or as services on the big cloud platforms. This course, Recognize the Need for Document Databases, introduces you to what document databases are, how they compare to the traditional relational databases, and some of the complexities involved when storing data on such systems.

First, you will explore what big data is, and how many of the requirements for big data analysis make document databases well-suited to manage their storage and analysis needs. While doing so, you will see some of the trade-offs involved in distributed systems when covering the CAP theorem.

Next, you will dive into the fundamentals of document databases - how data is represented in the form of key and value pairs, and the data formats which are used in such systems. You will also touch upon the benefits and drawbacks of representing data in the form of documents.

Finally, you will discover the modeling of data in document databases. Topics such as normalization and denormalization of data are covered, as are the modeling of relationships between different types of entities.

Once you complete this course, you will have a clear understanding of the characteristics of document databases - what their strengths are, and where such systems have limitations. You will be in a position to make informed decisions on when to use document databases, and how to model data on such platforms.

About the author
About the author

An engineer at heart, I am drawn to any interesting technical topic. Big Data, ML and Cloud are presently my topics of interest.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, and welcome to this course, Recognize the Need for Document Databases. My name is Kishan Iyer, and I will be your instructor for this course. A little about myself first. I have a masters degree in computer science from Columbia University and have previously worked in companies such as Deutsche Bank and WebMD in New York. I presently work for Loonycorn, a studio for high quality video content. Document databases have been gaining in popularity for a number of years now, and they are widely available as tools which can be downloaded and installed on your own servers, or as services on the big cloud platforms. This course introduces you to what document databases are, how they compare to the traditional relational databases, and some of the complexities involved when storing data on such systems. We begin by exploring what big data is and how many of the requirements for big data analysis make document databases well suited to manage their storage and analysis needs. While doing so, we discuss some of the trade‑offs involved in distributed systems when we cover the CAP theorem. The course then dives into the fundamentals of document databases, how data is represented in the form of key and value pairs, and the data formats which are valid in such systems. We also touch upon the benefits and drawbacks of representing data in the form of documents. Finally, we move along to the modeling of data in document databases. Topics such as normalization and de‑normalization of data are explored, as are the modeling of relationships between different types of entities. Once you complete this course, you will have a clearer understanding of the characteristics of document databases, what their strengths are, and where such systems have limitations. You will be in a position to make informed decisions on when to use document databases and how to model your data on such platforms.